[1] http://wedesoft.github.io/aiscm/
How does this compare with numpy?
TIA!
-- Amirouche ~ amz3 ~ http://www.hyperdev.fr
First of all numpy supports native floating point operations and
this work does not yet do that ;)
In terms of performance I previously posted some benchmarks
comparing with C programs [1].
Numpy is statically compiled and uses generic functions and function
pointers to implement binary operations for combinations of types [1].
np.array([1,2,3],dtype=np.uint8) + np.array([1,2,3],dtype=np.int16)
# array([2, 4, 6], dtype=int16)
When instead using a JIT (e.g. Python Theano) the resulting software
is more generic and composable in terms of operations (tensors,
composite operations) and in terms of datatypes (RGB, complex values,
arrays, hypercomplex values, ...).
Here's a technical report [3] with more details and motivation (in
that case libJIT and Ruby was used).
Please let me know if you have some interesting applications in mind
:)